Unpaired Image-to-Image Translation via a Self-Supervised Semantic Bridge
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arXiv
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| Format: | Preprint |
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2026
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| author | Liu, Jiaming Petersen, Felix Gao, Yunhe Zhang, Yabin Kim, Hyojin Chaudhari, Akshay S. Sun, Yu Ermon, Stefano Gatidis, Sergios |
| author_facet | Liu, Jiaming Petersen, Felix Gao, Yunhe Zhang, Yabin Kim, Hyojin Chaudhari, Akshay S. Sun, Yu Ermon, Stefano Gatidis, Sergios |
| contents | Adversarial diffusion and diffusion-inversion methods have advanced unpaired image-to-image translation, but each faces key limitations. Adversarial approaches require target-domain adversarial loss during training, which can limit generalization to unseen data, while diffusion-inversion methods often produce low-fidelity translations due to imperfect inversion into noise-latent representations. In this work, we propose the Self-Supervised Semantic Bridge (SSB), a versatile framework that integrates external semantic priors into diffusion bridge models to enable spatially faithful translation without cross-domain supervision. Our key idea is to leverage self-supervised visual encoders to learn representations that are invariant to appearance changes but capture geometric structure, forming a shared latent space that conditions the diffusion bridges. Extensive experiments show that SSB outperforms strong prior methods for challenging medical image synthesis in both in-domain and out-of-domain settings, and extends easily to high-quality text-guided editing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_16664 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Unpaired Image-to-Image Translation via a Self-Supervised Semantic Bridge Liu, Jiaming Petersen, Felix Gao, Yunhe Zhang, Yabin Kim, Hyojin Chaudhari, Akshay S. Sun, Yu Ermon, Stefano Gatidis, Sergios Computer Vision and Pattern Recognition Adversarial diffusion and diffusion-inversion methods have advanced unpaired image-to-image translation, but each faces key limitations. Adversarial approaches require target-domain adversarial loss during training, which can limit generalization to unseen data, while diffusion-inversion methods often produce low-fidelity translations due to imperfect inversion into noise-latent representations. In this work, we propose the Self-Supervised Semantic Bridge (SSB), a versatile framework that integrates external semantic priors into diffusion bridge models to enable spatially faithful translation without cross-domain supervision. Our key idea is to leverage self-supervised visual encoders to learn representations that are invariant to appearance changes but capture geometric structure, forming a shared latent space that conditions the diffusion bridges. Extensive experiments show that SSB outperforms strong prior methods for challenging medical image synthesis in both in-domain and out-of-domain settings, and extends easily to high-quality text-guided editing. |
| title | Unpaired Image-to-Image Translation via a Self-Supervised Semantic Bridge |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2602.16664 |